MRG: Present is the __future__ (#5726)

* ENH: Present is the __future__

* FIX: Bump versions to 3.5-compat

* FIX: Bump dependencies to 3.5 compat

* FIX: Permissive lib

* ENH: No more six

* DOC: whats_new.rst

* Towards a green travis (#32)

* FIX: use py3 dictionary iterator

* FIX: remove six.string_types

* Fix: bytecodes

* Fix: remove advance_iterator

* FIX: more encoding

* Fix: add StringIO from io

* FIX: Imports

* FIX: Heisenbugs

* FIX: Fixes for 3.5

* FIX: Ignore

.circleci/config.yml

@@ -258,7 +258,7 @@ jobs:
                 git config --global user.email "circle@mne.com";
                 git config --global user.name "Circle Ci";
                 pushd ~/mne-tools.github.io && git checkout master && git pull origin master && popd;
-                pushd doc/_build && rm -Rf ~/mne-tools.github.io/stable cp -a html_stable ~/mne-tools.github.io/stable && popd;
+                pushd doc/_build && rm -Rf ~/mne-tools.github.io/stable && cp -a html_stable ~/mne-tools.github.io/stable && popd;
                 pushd ~/mne-tools.github.io && git add -A && git commit -m "CircleCI update of stable docs (${CIRCLE_BUILD_NUM})." && git push origin master && popd;
               else
                 echo "No deployment (build: ${CIRCLE_BRANCH}).";

.travis.yml

@@ -36,10 +36,10 @@ matrix:
           language: python
           python: "3.6"
 
-        # 2.7 Old dependencies
+        # Old dependencies
         - os: linux
-          env: PYTHON_VERSION=2.7
-               CONDA_DEPENDENCIES="numpy=1.8 scipy=0.12 matplotlib=1.3 pandas=0.13 scikit-learn=0.15 pytest pytest-cov"
+          env: PYTHON_VERSION=3.5
+               CONDA_DEPENDENCIES="numpy=1.11 scipy=0.17 matplotlib=1.5 pandas=0.18 scikit-learn=0.18 pytest pytest-cov"
 
         # Minimal
         - os: linux

README.rst

@@ -78,18 +78,18 @@ Dependencies
 
 The minimum required dependencies to run MNE-Python are:
 
-- Python >= 2.7
-- NumPy >= 1.8
-- SciPy >= 0.12
+- Python >= 3.5
+- NumPy >= 1.11.3
+- SciPy >= 0.17.1
 
 For full functionality, some functions require:
 
-- Matplotlib >= 1.3
+- Matplotlib >= 1.5
 - Mayavi >= 4.6
 - PySurfer >= 0.8
-- Scikit-learn >= 0.15 (>= 0.18 recommended)
+- Scikit-learn >= 0.18
 - NiBabel >= 2.1.0
-- Pandas >= 0.13
+- Pandas >= 0.18
 - Picard >= 0.3
 - CuPy >= 4.0 (for NVIDIA CUDA acceleration)
 - DIPY >= 0.10.1

doc/configure_git.rst

@@ -350,7 +350,8 @@ When you are ready to ask for someone to review your code and consider a merge:
    how the code should be written (features, style, etc.) that are easier to
    implement from the start.
 
-#. Finally, make `travis`_ happy. Ensure that builds in all four jobs pass. To make code python3 compatible, refer to ``externals/six.py``. Use virtual environments to test code on different python versions. Please remember that `travis`_ only runs a subset of the tests and is thus not a substitute for running the entire test suite locally.
+#. Finally, make `travis`_ happy. Ensure that builds in all four jobs pass.
+   Use virtual environments to test code on different python versions.
 
 #. For the code to be mergeable, please rebase w.r.t master branch.
 

doc/install_mne_python.rst

@@ -23,7 +23,7 @@ When you are done, you should see a similar output if you type the following com
 If you get an error message, consult the Anaconda documentation and search for Anaconda install
 tips (`Stack Overflow <https://stackoverflow.com/>`_ results are often helpful).
 
-.. note:: MNE-Python 0.17 will be the last release to support Python 2. From MNE-Python 0.18, only Python 3 will be supported.
+.. note:: MNE-Python 0.18 only supports Python 3.5+.
 
 2. Get MNE and its dependencies
 ###############################

doc/sphinxext/gen_commands.py

@@ -1,7 +1,5 @@
 # -*- coding: utf-8 -*-
 
-from __future__ import print_function
-
 import os
 import glob
 from os import path as op

doc/whats_new.rst

@@ -27,6 +27,8 @@ Bug
 API
 ~~~
 
+- Python 2 is no longer supported; MNE-Python now requires Python 3.5+, by `Eric Larson`_
+
 .. _changes_0_17:
 
 Version 0.17

mne/annotations.py

@@ -15,7 +15,6 @@
 from .utils import _pl, check_fname, _validate_type, verbose, warn, logger
 from .utils import _check_pandas_installed
 from .utils import _Counter as Counter
-from .externals.six import string_types
 from .io.write import (start_block, end_block, write_float, write_name_list,
                        write_double, start_file)
 from .io.constants import FIFF
@@ -161,7 +160,7 @@ def __init__(self, onset, duration, description,
                              '(shape %s).'
                              % (onset.ndim, onset.shape))
         duration = np.array(duration, dtype=float)
-        if isinstance(description, string_types):
+        if isinstance(description, str):
             description = np.repeat(description, len(onset))
         if duration.ndim != 1:
             raise ValueError('Duration must be a one dimensional array.')
@@ -384,7 +383,7 @@ def _handle_meas_date(meas_date):
     """
     if meas_date is None:
         meas_date = 0
-    elif isinstance(meas_date, string_types):
+    elif isinstance(meas_date, str):
         ACCEPTED_ISO8601 = '%Y-%m-%d %H:%M:%S.%f'
         try:
             meas_date = datetime.strptime(meas_date, ACCEPTED_ISO8601)
@@ -420,9 +419,9 @@ def _annotations_starts_stops(raw, kinds, name='unknown', invert=False):
 
     onsets and ends are inclusive.
     """
-    _validate_type(kinds, (string_types, list, tuple), str(type(kinds)),
+    _validate_type(kinds, (str, list, tuple), str(type(kinds)),
                    "str, list or tuple")
-    if isinstance(kinds, string_types):
+    if isinstance(kinds, str):
         kinds = [kinds]
     else:
         for kind in kinds:
@@ -486,8 +485,8 @@ def _write_annotations_txt(fname, annot):
 
     data = np.array([annot.onset, annot.duration, annot.description],
                     dtype=str).T
-    with open(fname, 'w') as fid:
-        fid.write(content)
+    with open(fname, 'wb') as fid:
+        fid.write(content.encode())
         np.savetxt(fid, data, delimiter=',', fmt="%s")
 
 
@@ -654,10 +653,10 @@ def is_orig_time(x):
 
 def _read_annotations_txt(fname):
     onset, duration, desc = np.loadtxt(fname, delimiter=',',
-                                       dtype=str, unpack=True)
-    onset = [float(o) for o in onset]
-    duration = [float(d) for d in duration]
-    desc = [str(d).strip() for d in desc]
+                                       dtype=np.bytes_, unpack=True)
+    onset = [float(o.decode()) for o in onset]
+    duration = [float(d.decode()) for d in duration]
+    desc = [str(d.decode()).strip() for d in desc]
     return onset, duration, desc
 
 

mne/beamformer/_compute_beamformer.py

@@ -23,12 +23,11 @@
 from ..time_frequency.csd import CrossSpectralDensity
 
 from ..externals.h5io import read_hdf5, write_hdf5
-from ..externals.six import string_types
 
 
 def _check_rank(rank):
     """Check rank parameter and deal with deprecation."""
-    if isinstance(rank, string_types):
+    if isinstance(rank, str):
         # XXX we can use rank='' to deprecate to get to None eventually:
         # if rank == '':
         #     warn('The rank parameter default in 0.18 of "full" will change '

mne/beamformer/_dics.py

@@ -18,7 +18,6 @@
                                   _check_proj_match, _prepare_beamformer_input,
                                   _compute_beamformer, _check_one_ch_type,
                                   _check_src_type, Beamformer, _check_rank)
-from ..externals import six
 
 
 @verbose
@@ -361,7 +360,7 @@ def apply_dics(evoked, filters, verbose=None):
 
     stc = _apply_dics(data=data, filters=filters, info=info, tmin=tmin)
 
-    return six.advance_iterator(stc)
+    return next(stc)
 
 
 @verbose

mne/beamformer/_lcmv.py

@@ -15,7 +15,6 @@
 from ..source_estimate import _make_stc, SourceEstimate, _get_src_type
 from ..utils import logger, verbose, warn, _validate_type, _reg_pinv
 from .. import Epochs
-from ..externals import six
 from ._compute_beamformer import (
     _setup_picks, _pick_channels_spatial_filter,
     _check_proj_match, _prepare_beamformer_input, _check_one_ch_type,
@@ -301,7 +300,7 @@ def apply_lcmv(evoked, filters, max_ori_out='signed', verbose=None):
     stc = _apply_lcmv(data=data, filters=filters, info=info,
                       tmin=tmin, max_ori_out=max_ori_out)
 
-    return six.advance_iterator(stc)
+    return next(stc)
 
 
 @verbose
@@ -402,7 +401,7 @@ def apply_lcmv_raw(raw, filters, start=None, stop=None, max_ori_out='signed',
     stc = _apply_lcmv(data=data, filters=filters, info=info,
                       tmin=tmin, max_ori_out=max_ori_out)
 
-    return six.advance_iterator(stc)
+    return next(stc)
 
 
 @verbose

mne/beamformer/tests/test_dics.py

@@ -18,7 +18,6 @@
                             Beamformer)
 from mne.time_frequency import csd_morlet
 from mne.utils import run_tests_if_main, object_diff, requires_h5py
-from mne.externals.six import advance_iterator
 from mne.proj import compute_proj_evoked, make_projector
 
 data_path = testing.data_path(download=False)
@@ -418,7 +417,7 @@ def test_apply_dics_timeseries():
     # Test returning a generator
     stcs = apply_dics_epochs(epochs, filters, return_generator=False)
     stcs_gen = apply_dics_epochs(epochs, filters, return_generator=True)
-    assert_array_equal(stcs[0].data, advance_iterator(stcs_gen).data)
+    assert_array_equal(stcs[0].data, next(stcs_gen).data)
 
     # Test computing timecourses on a volume source space
     filters_vol = make_dics(evoked.info, fwd_vol, csd20, reg=reg)

mne/beamformer/tests/test_lcmv.py

@@ -16,7 +16,6 @@
                             read_beamformer)
 from mne.beamformer._lcmv import _lcmv_source_power
 from mne.minimum_norm import make_inverse_operator, apply_inverse
-from mne.externals.six import advance_iterator
 from mne.simulation import simulate_evoked
 from mne.utils import run_tests_if_main, object_diff, requires_h5py
 
@@ -392,7 +391,7 @@ def test_make_lcmv(tmpdir):
     stcs = apply_lcmv_epochs(epochs, filters, max_ori_out='signed')
     stcs_ = apply_lcmv_epochs(epochs, filters, return_generator=True,
                               max_ori_out='signed')
-    assert_array_equal(stcs[0].data, advance_iterator(stcs_).data)
+    assert_array_equal(stcs[0].data, next(stcs_).data)
 
     epochs.drop_bad()
     assert (len(epochs.events) == len(stcs))

mne/bem.py

@@ -29,7 +29,6 @@
 from .utils import (verbose, logger, run_subprocess, get_subjects_dir, warn,
                     _pl, _validate_type)
 from .fixes import einsum
-from .externals.six import string_types
 
 
 # ############################################################################
@@ -312,7 +311,7 @@ def make_bem_solution(surfs, verbose=None):
     write_bem_solution
     """
     logger.info('Approximation method : Linear collocation\n')
-    if isinstance(surfs, string_types):
+    if isinstance(surfs, str):
         # Load the surfaces
         logger.info('Loading surfaces...')
         surfs = read_bem_surfaces(surfs)
@@ -478,7 +477,7 @@ def _surfaces_to_bem(surfs, ids, sigmas, ico=None, rescale=True,
                          'number of elements (1 or 3)')
     surf = list(surfs)
     for si, surf in enumerate(surfs):
-        if isinstance(surf, string_types):
+        if isinstance(surf, str):
             surfs[si] = read_surface(surf, return_dict=True)[-1]
     # Downsampling if the surface is isomorphic with a subdivided icosahedron
     if ico is not None:
@@ -736,7 +735,7 @@ def make_sphere_model(r0=(0., 0., 0.04), head_radius=0.09, info=None,
     """
     for name in ('r0', 'head_radius'):
         param = locals()[name]
-        if isinstance(param, string_types):
+        if isinstance(param, str):
             if param != 'auto':
                 raise ValueError('%s, if str, must be "auto" not "%s"'
                                  % (name, param))
@@ -749,14 +748,14 @@ def make_sphere_model(r0=(0., 0., 0.04), head_radius=0.09, info=None,
     if len(sigmas) <= 1 and head_radius is not None:
         raise ValueError('at least 2 sigmas must be supplied if '
                          'head_radius is not None, got %s' % (len(sigmas),))
-    if (isinstance(r0, string_types) and r0 == 'auto') or \
-       (isinstance(head_radius, string_types) and head_radius == 'auto'):
+    if (isinstance(r0, str) and r0 == 'auto') or \
+       (isinstance(head_radius, str) and head_radius == 'auto'):
         if info is None:
             raise ValueError('Info must not be None for auto mode')
         head_radius_fit, r0_fit = fit_sphere_to_headshape(info, units='m')[:2]
-        if isinstance(r0, string_types):
+        if isinstance(r0, str):
             r0 = r0_fit
-        if isinstance(head_radius, string_types):
+        if isinstance(head_radius, str):
             head_radius = head_radius_fit
     sphere = ConductorModel(is_sphere=True, r0=np.array(r0),
                             coord_frame=FIFF.FIFFV_COORD_HEAD)
@@ -851,7 +850,7 @@ def fit_sphere_to_headshape(info, dig_kinds='auto', units='m', verbose=None):
     This function excludes any points that are low and frontal
     (``z < 0 and y > 0``) to improve the fit.
     """
-    if not isinstance(units, string_types) or units not in ('m', 'mm'):
+    if not isinstance(units, str) or units not in ('m', 'mm'):
         raise ValueError('units must be a "m" or "mm"')
     radius, origin_head, origin_device = _fit_sphere_to_headshape(
         info, dig_kinds)
@@ -895,7 +894,7 @@ def get_fitting_dig(info, dig_kinds='auto', verbose=None):
     if info['dig'] is None:
         raise RuntimeError('Cannot fit headshape without digitization '
                            ', info["dig"] is None')
-    if isinstance(dig_kinds, string_types):
+    if isinstance(dig_kinds, str):
         if dig_kinds == 'auto':
             # try "extra" first
             try:
@@ -964,7 +963,7 @@ def _fit_sphere_to_headshape(info, dig_kinds, verbose=None):
 def _fit_sphere(points, disp='auto'):
     """Fit a sphere to an arbitrary set of points."""
     from scipy.optimize import fmin_cobyla
-    if isinstance(disp, string_types) and disp == 'auto':
+    if isinstance(disp, str) and disp == 'auto':
         disp = True if logger.level <= 20 else False
     # initial guess for center and radius
     radii = (np.max(points, axis=1) - np.min(points, axis=1)) / 2.
@@ -992,7 +991,7 @@ def constraint(center_rad):
 
 def _check_origin(origin, info, coord_frame='head', disp=False):
     """Check or auto-determine the origin."""
-    if isinstance(origin, string_types):
+    if isinstance(origin, str):
         if origin != 'auto':
             raise ValueError('origin must be a numerical array, or "auto", '
                              'not %s' % (origin,))
@@ -1438,7 +1437,7 @@ def _add_gamma_multipliers(bem):
 
 def _bem_find_surface(bem, id_):
     """Find surface from already-loaded BEM."""
-    if isinstance(id_, string_types):
+    if isinstance(id_, str):
         name = id_
         id_ = _surf_dict[id_]
     else:

mne/channels/channels.py

@@ -13,7 +13,6 @@
 import numpy as np
 from scipy import sparse
 
-from ..externals.six import string_types
 from ..utils import verbose, logger, warn, copy_function_doc_to_method_doc
 from ..utils import _check_preload, _validate_type
 from ..io.compensator import get_current_comp
@@ -779,10 +778,10 @@ def drop_channels(self, ch_names):
         """
         msg = ("'ch_names' should be a list of strings (the name[s] of the "
                "channel to be dropped), not a {0}.")
-        if isinstance(ch_names, string_types):
+        if isinstance(ch_names, str):
             raise ValueError(msg.format("string"))
         else:
-            if not all([isinstance(ch_name, string_types)
+            if not all([isinstance(ch_name, str)
                         for ch_name in ch_names]):
                 raise ValueError(msg.format(type(ch_names[0])))
 
@@ -1088,8 +1087,8 @@ def read_ch_connectivity(fname, picks=None):
         fname = op.join(templates_dir, fname)
 
     nb = loadmat(fname)['neighbours']
-    ch_names = _recursive_flatten(nb['label'], string_types)
-    neighbors = [_recursive_flatten(c, string_types) for c in
+    ch_names = _recursive_flatten(nb['label'], str)
+    neighbors = [_recursive_flatten(c, str) for c in
                  nb['neighblabel'].flatten()]
     assert len(ch_names) == len(neighbors)
     if picks is not None:
@@ -1134,7 +1133,7 @@ def _ch_neighbor_connectivity(ch_names, neighbors):
 
     for neigh in neighbors:
         if (not isinstance(neigh, list) and
-                not all(isinstance(c, string_types) for c in neigh)):
+                not all(isinstance(c, str) for c in neigh)):
             raise ValueError('`neighbors` must be a list of lists of str')
 
     ch_connectivity = np.eye(len(ch_names), dtype=bool)

mne/channels/layout.py

@@ -21,7 +21,6 @@
 from ..io.constants import FIFF
 from ..io.meas_info import Info
 from ..utils import _clean_names, warn, _check_ch_locs
-from ..externals.six.moves import map
 from .channels import _get_ch_info